subimanova(1) subimanova(1)NAMEsubimanova - subtracts image averages with analysis of varianceSYNOPSISsubimanovaDESCRIPTIONSUBIMANOVA subtracts one set of average images from another set and uses a nested analysis of variance (ANOVA) to find the statistical sig- nificance of the difference at each pixel. It then sets to zero all differences less significant than a specified level. The program can output either actual differences or pixel values that reflect the level of significance. In order to do the ANOVA, it must have a standard deviation or variance image corresponding to each average image. The average and standard deviation/variance images can be ones produced by IMAVGSTAT or by other means. When one starts the program, one des- ignates a pair of A files (with average and S.D./VAR images) and a pair of B files. One can then subtract any set of sections in B from any set of sections in A; A and B may be the same pair of files. The user is responsible for keeping track of how many samples were used in making each average, and informing this program of those numbers. The program needs these numbers to do the ANOVA. Entries to the program: Average image file A Standard deviation or variance image file A Average image file B, or Return if same as file for A Standard deviation or variance image file B, or Return if same as file for A Output image file to store differences in 0 to use a simple mean when combining the average images of one set, or 1 to form a weighted mean, where each average image would be weighted by the number of samples combined to form that average. In the latter case, the mean would be identical to the average image that could be obtain by combining ALL of the samples of that set. 0 if the files have standard deviations in them, or 1 if the files have variances Number of differences to compute For each difference, enter: List of section numbers in file A, where ranges are allowed (e.g. 0-2,4,7-8). List of section numbers in file B, where ranges are allowed Number of samples making up those averages for each section in A Number of samples making up those averages for each section in B Significance level (e.g. 0.05, 0.01, etc). Differences with less than this significance will be set to zero. Enter a negative value to have significant pixels values set to the negative of the log of the probability, or to the positive log for negative differences. For example, positive and negative differences with a P of 0.01 would be output as 2 and -2, respectively. The infamous Satterthwaite approximation will be used whenever the cri- teria for its application are satisfied.HISTORYWritten by David Mastronarde, 4/23/90 4/12/95 changed to use local subroutines instead of NAG onesBUGSEmail bug reports to mast at colorado dot edu. IMOD 4.9.6 subimanova(1)